This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Background: This TRD is concerned with the estimation of biomedically meaningful parameters and values, which are either outputs of the other CIBC TRDs or are needed as inputs or parameters for those TRDs. It includes both direct estimation from data and "indirect" estimation via solutions to inverse problems. Broadly speaking, The goals of the CIBC all stem from the necessity to enable biomedical science through the application of image- based technology, and to better solve a wide range of underlying biomedical problems. Rationale: The Estimation TRD is closely tied to the general theme of this CIBC renewal: individualized (subject-specific) imaging, modeling, and simulation. In particular, it concentrates on treating uncertainty in these fields of biomedical imaging. In turn, our ability to do so is directly tied to another of our main goals, increasing reproducibility of image-based biomedical research. Broadly speaking, achieving the goals we address will improve the ability of biomedical and computational scientists to: extract quantitative biomedical information from image-based diagnostic tests and therapeutic procedures; robustly compare results across common algorithms and even modalities in a repeatable and quantitative manner;identify, compare, and test critical results;and place such results in the context of population statistics or other contextual information. Questions: A particular area for further development is our Matlab-based optimization methods for non-linear inverse problems. Although they've have worked well in the past, we plan to gradually expand SCIRun's infrastructure to include iterative optimization schemes, primarily basic Newton-type methods. Adding this capability to SCIRun will have several advantages, including lessening our dependence on proprietary software, the potential for more tailored and efficient algorithms, and the ability to tie in more closely with our planned GPU-based acceleration of simulations. Design &Methods: We treat various kinds of estimation in a single TRD framework, all in the context of computations based on the assumption of uncertainty in our measurements and modeling. In turn, these estimation results are often themselves useful to guide image- based modeling efforts such as mesh construction, or simulation efforts such as of bioelectric fields produced by stimulation, or they become features of data that need effective presentation to the user via novel visualization and visual analysis techniques, such as statistics based on shape models or space-varying uncertainty of inverse solution results.